from sklearn_benchmarks.report import Reporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

scikit-learn vs. scikit-learn-intelex (Intel® oneAPI) benchmarks: perfect hyperparameters match

Reporting(config="config.yml").run()

KNeighborsClassifier: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: algorithm=brute.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.135	0.0	5.922	0.0	1	100	NaN	NaN	0.486	0.0	0.278	0.0	See	See
3	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.128	0.0	6.259	0.0	-1	100	NaN	NaN	0.470	0.0	0.272	0.0	See	See
6	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.130	0.0	6.149	0.0	-1	1	NaN	NaN	0.470	0.0	0.277	0.0	See	See
9	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.126	0.0	6.350	0.0	1	5	NaN	NaN	0.471	0.0	0.267	0.0	See	See
12	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.124	0.0	6.435	0.0	1	1	NaN	NaN	0.470	0.0	0.264	0.0	See	See
15	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.124	0.0	6.462	0.0	-1	5	NaN	NaN	0.472	0.0	0.262	0.0	See	See
18	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.054	0.0	0.296	0.0	1	100	NaN	NaN	0.100	0.0	0.541	0.0	See	See
21	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.058	0.0	0.278	0.0	-1	100	NaN	NaN	0.098	0.0	0.589	0.0	See	See
24	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.055	0.0	0.291	0.0	-1	1	NaN	NaN	0.098	0.0	0.562	0.0	See	See
27	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.073	0.0	0.220	0.0	1	5	NaN	NaN	0.098	0.0	0.740	0.0	See	See
30	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.055	0.0	0.293	0.0	1	1	NaN	NaN	0.102	0.0	0.537	0.0	See	See
33	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.054	0.0	0.295	0.0	-1	5	NaN	NaN	0.098	0.0	0.552	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier	predict	1000000	1000	100	NaN	21.523	0.054	0.0	0.022	1	100	0.943	0.684	1.738	0.005	12.382	0.048	See	See
2	KNeighborsClassifier	predict	1000000	1	100	NaN	0.209	0.002	0.0	0.209	1	100	1.000	0.000	0.087	0.000	2.410	0.024	See	See
4	KNeighborsClassifier	predict	1000000	1000	100	NaN	35.152	0.000	0.0	0.035	-1	100	0.943	0.927	1.783	0.006	19.713	0.061	See	See
5	KNeighborsClassifier	predict	1000000	1	100	NaN	0.191	0.015	0.0	0.191	-1	100	1.000	1.000	0.087	0.000	2.194	0.167	See	See
7	KNeighborsClassifier	predict	1000000	1000	100	NaN	26.514	0.109	0.0	0.027	-1	1	0.703	0.792	1.737	0.004	15.267	0.072	See	See
8	KNeighborsClassifier	predict	1000000	1	100	NaN	0.181	0.015	0.0	0.181	-1	1	0.000	1.000	0.088	0.002	2.066	0.180	See	See
10	KNeighborsClassifier	predict	1000000	1000	100	NaN	21.631	0.036	0.0	0.022	1	5	0.807	0.792	1.737	0.004	12.453	0.034	See	See
11	KNeighborsClassifier	predict	1000000	1	100	NaN	0.206	0.001	0.0	0.206	1	5	1.000	1.000	0.087	0.001	2.363	0.016	See	See
13	KNeighborsClassifier	predict	1000000	1000	100	NaN	12.811	0.220	0.0	0.013	1	1	0.703	0.684	1.738	0.004	7.372	0.128	See	See
14	KNeighborsClassifier	predict	1000000	1	100	NaN	0.196	0.000	0.0	0.196	1	1	0.000	0.000	0.088	0.000	2.235	0.011	See	See
16	KNeighborsClassifier	predict	1000000	1000	100	NaN	35.288	0.000	0.0	0.035	-1	5	0.807	0.927	1.784	0.004	19.782	0.047	See	See
17	KNeighborsClassifier	predict	1000000	1	100	NaN	0.202	0.014	0.0	0.202	-1	5	1.000	1.000	0.088	0.001	2.300	0.162	See	See
19	KNeighborsClassifier	predict	1000000	1000	2	NaN	20.001	0.048	0.0	0.020	1	100	0.987	0.973	0.251	0.000	79.740	0.228	See	See
20	KNeighborsClassifier	predict	1000000	1	2	NaN	0.027	0.001	0.0	0.027	1	100	1.000	1.000	0.005	0.000	5.487	0.470	See	See
22	KNeighborsClassifier	predict	1000000	1000	2	NaN	33.915	0.000	0.0	0.034	-1	100	0.987	0.985	0.298	0.000	113.619	0.167	See	See
23	KNeighborsClassifier	predict	1000000	1	2	NaN	0.035	0.002	0.0	0.035	-1	100	1.000	1.000	0.005	0.000	6.954	0.800	See	See
25	KNeighborsClassifier	predict	1000000	1000	2	NaN	24.380	0.124	0.0	0.024	-1	1	0.978	0.983	0.253	0.001	96.522	0.570	See	See
26	KNeighborsClassifier	predict	1000000	1	2	NaN	0.022	0.002	0.0	0.022	-1	1	1.000	1.000	0.005	0.000	4.304	0.555	See	See
28	KNeighborsClassifier	predict	1000000	1000	2	NaN	20.129	0.025	0.0	0.020	1	5	0.983	0.983	0.253	0.001	79.423	0.200	See	See
29	KNeighborsClassifier	predict	1000000	1	2	NaN	0.028	0.000	0.0	0.028	1	5	1.000	1.000	0.005	0.000	5.173	0.409	See	See
31	KNeighborsClassifier	predict	1000000	1000	2	NaN	10.190	0.018	0.0	0.010	1	1	0.978	0.973	0.251	0.000	40.616	0.087	See	See
32	KNeighborsClassifier	predict	1000000	1	2	NaN	0.015	0.000	0.0	0.015	1	1	1.000	1.000	0.005	0.000	2.998	0.258	See	See
34	KNeighborsClassifier	predict	1000000	1000	2	NaN	34.033	0.000	0.0	0.034	-1	5	0.983	0.985	0.299	0.001	113.804	0.271	See	See
35	KNeighborsClassifier	predict	1000000	1	2	NaN	0.035	0.002	0.0	0.035	-1	5	1.000	1.000	0.005	0.000	6.712	0.774	See	See

KNeighborsClassifier_kd_tree: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: algorithm=kd_tree.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.809	0.0	0.028	0.0	-1	5	NaN	NaN	0.729	0.0	3.855	0.0	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.819	0.0	0.028	0.0	1	1	NaN	NaN	0.722	0.0	3.903	0.0	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.825	0.0	0.028	0.0	1	5	NaN	NaN	0.727	0.0	3.888	0.0	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.832	0.0	0.028	0.0	-1	100	NaN	NaN	0.730	0.0	3.879	0.0	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.794	0.0	0.029	0.0	1	100	NaN	NaN	0.731	0.0	3.824	0.0	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.803	0.0	0.029	0.0	-1	1	NaN	NaN	0.729	0.0	3.846	0.0	See	See
18	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.730	0.0	0.022	0.0	-1	5	NaN	NaN	0.487	0.0	1.498	0.0	See	See
21	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.727	0.0	0.022	0.0	1	1	NaN	NaN	0.476	0.0	1.528	0.0	See	See
24	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.724	0.0	0.022	0.0	1	5	NaN	NaN	0.441	0.0	1.643	0.0	See	See
27	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.728	0.0	0.022	0.0	-1	100	NaN	NaN	0.435	0.0	1.675	0.0	See	See
30	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.723	0.0	0.022	0.0	1	100	NaN	NaN	0.493	0.0	1.467	0.0	See	See
33	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.729	0.0	0.022	0.0	-1	1	NaN	NaN	0.463	0.0	1.575	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.851	0.006	0.000	0.001	-1	5	0.977	0.962	0.200	0.002	4.256	0.044	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.003	0.000	0.000	0.003	-1	5	1.000	1.000	0.000	0.000	6.109	3.180	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.739	0.005	0.000	0.001	1	1	0.966	0.964	0.603	0.005	1.225	0.013	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.001	0.000	0.813	0.430	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	1.436	0.008	0.000	0.001	1	5	0.977	0.954	0.111	0.003	12.939	0.325	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.001	0.000	0.000	0.001	1	5	1.000	1.000	0.000	0.000	3.657	2.099	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	2.832	0.030	0.000	0.003	-1	100	0.972	0.962	0.209	0.005	13.533	0.383	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.004	0.001	0.000	0.004	-1	100	1.000	1.000	0.000	0.000	9.764	5.354	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	4.873	0.037	0.000	0.005	1	100	0.972	0.954	0.109	0.002	44.878	0.932	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.002	0.001	0.000	0.002	1	100	1.000	1.000	0.000	0.000	8.524	5.306	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.440	0.004	0.000	0.000	-1	1	0.966	0.964	0.604	0.007	0.728	0.011	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.003	0.000	0.000	0.003	-1	1	1.000	1.000	0.001	0.000	2.555	1.246	See	See
19	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.023	0.001	0.001	0.000	-1	5	0.982	0.981	0.001	0.000	20.882	5.852	See	See
20	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.002	0.000	0.000	0.002	-1	5	1.000	1.000	0.000	0.000	20.613	17.626	See	See
22	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.020	0.001	0.001	0.000	1	1	0.974	0.983	0.006	0.001	3.217	0.401	See	See
23	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	4.964	4.377	See	See
25	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.021	0.001	0.001	0.000	1	5	0.982	0.972	0.001	0.000	27.907	11.048	See	See
26	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	5	1.000	1.000	0.000	0.000	5.746	4.832	See	See
28	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.040	0.002	0.000	0.000	-1	100	0.984	0.981	0.001	0.000	36.624	10.196	See	See
29	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.002	0.000	0.000	0.002	-1	100	1.000	1.000	0.000	0.000	20.823	17.587	See	See
31	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.045	0.003	0.000	0.000	1	100	0.984	0.972	0.001	0.000	62.903	22.168	See	See
32	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	100	1.000	1.000	0.000	0.000	5.684	4.774	See	See
34	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.021	0.001	0.001	0.000	-1	1	0.974	0.983	0.006	0.001	3.394	0.479	See	See
35	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.002	0.000	0.000	0.002	-1	1	1.000	1.000	0.000	0.000	17.999	14.323	See	See

KMeans_tall: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_tall	fit	1000000	1000000	2	30	0.556	0.0	0.863	0.0	random	NaN	30	NaN	0.382	0.0	1.456	0.0	See	See
3	KMeans_tall	fit	1000000	1000000	2	30	0.561	0.0	0.856	0.0	k-means++	NaN	30	NaN	0.408	0.0	1.373	0.0	See	See
6	KMeans_tall	fit	1000000	1000000	100	30	6.301	0.0	3.809	0.0	random	NaN	30	NaN	2.611	0.0	2.413	0.0	See	See
9	KMeans_tall	fit	1000000	1000000	100	30	6.186	0.0	3.880	0.0	k-means++	NaN	30	NaN	2.769	0.0	2.234	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_tall	predict	1000000	1000	2	30	0.001	0.0	0.360	0.000	random	0.001	30	0.001	0.0	0.0	9.662	6.921	See	See
2	KMeans_tall	predict	1000000	1	2	30	0.001	0.0	0.000	0.001	random	1.000	30	1.000	0.0	0.0	10.607	8.210	See	See
4	KMeans_tall	predict	1000000	1000	2	30	0.001	0.0	0.363	0.000	k-means++	0.001	30	0.001	0.0	0.0	9.179	6.192	See	See
5	KMeans_tall	predict	1000000	1	2	30	0.001	0.0	0.000	0.001	k-means++	1.000	30	1.000	0.0	0.0	10.868	7.533	See	See
7	KMeans_tall	predict	1000000	1000	100	30	0.002	0.0	14.949	0.000	random	0.002	30	0.002	0.0	0.0	6.190	3.222	See	See
8	KMeans_tall	predict	1000000	1	100	30	0.001	0.0	0.019	0.001	random	1.000	30	1.000	0.0	0.0	10.064	7.081	See	See
10	KMeans_tall	predict	1000000	1000	100	30	0.002	0.0	15.562	0.000	k-means++	0.002	30	0.002	0.0	0.0	5.875	2.812	See	See
11	KMeans_tall	predict	1000000	1	100	30	0.001	0.0	0.019	0.001	k-means++	1.000	30	1.000	0.0	0.0	10.052	6.738	See	See

KMeans_short: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_short	fit	10000	10000	2	20	0.073	0.0	0.044	0.0	random	NaN	20	NaN	0.081	0.0	0.900	0.0	See	See
3	KMeans_short	fit	10000	10000	2	20	0.204	0.0	0.016	0.0	k-means++	NaN	20	NaN	0.026	0.0	7.694	0.0	See	See
6	KMeans_short	fit	10000	10000	100	20	0.186	0.0	0.860	0.0	random	NaN	20	NaN	0.286	0.0	0.650	0.0	See	See
9	KMeans_short	fit	10000	10000	100	20	0.555	0.0	0.289	0.0	k-means++	NaN	20	NaN	0.104	0.0	5.319	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_short	predict	10000	1000	2	20	0.002	0.0	0.186	0.000	random	0.000	20	0.001	0.001	0.0	3.371	0.826	See	See
2	KMeans_short	predict	10000	1	2	20	0.001	0.0	0.000	0.001	random	1.000	20	1.000	0.000	0.0	10.074	6.663	See	See
4	KMeans_short	predict	10000	1000	2	20	0.002	0.0	0.189	0.000	k-means++	0.000	20	0.003	0.000	0.0	3.482	0.723	See	See
5	KMeans_short	predict	10000	1	2	20	0.001	0.0	0.000	0.001	k-means++	1.000	20	1.000	0.000	0.0	10.018	6.551	See	See
7	KMeans_short	predict	10000	1000	100	20	0.002	0.0	6.858	0.000	random	0.255	20	0.259	0.001	0.0	2.319	0.393	See	See
8	KMeans_short	predict	10000	1	100	20	0.001	0.0	0.012	0.001	random	1.000	20	1.000	0.000	0.0	8.759	5.501	See	See
10	KMeans_short	predict	10000	1000	100	20	0.002	0.0	6.726	0.000	k-means++	0.238	20	0.360	0.001	0.0	2.407	0.420	See	See
11	KMeans_short	predict	10000	1	100	20	0.001	0.0	0.012	0.001	k-means++	1.000	20	1.000	0.000	0.0	8.700	5.154	See	See

LogisticRegression: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	LogisticRegression	fit	1000000	1000000	100	[20]	11.724	0.0	[-0.10063635]	0.000	NaN	NaN	NaN	NaN	NaN	1.974	0.0	5.939	0.0	See	See
3	LogisticRegression	fit	1000	1000	10000	[26]	0.836	0.0	[2.48752162]	0.001	NaN	NaN	NaN	NaN	NaN	0.850	0.0	0.984	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	LogisticRegression	predict	1000000	1000	100	[20]	0.000	0.0	[56.37524774]	0.0	NaN	NaN	NaN	NaN	0.541	0.000	0.0	0.849	0.458	See	See
2	LogisticRegression	predict	1000000	1	100	[20]	0.000	0.0	[0.26083909]	0.0	NaN	NaN	NaN	NaN	0.000	0.000	0.0	0.366	0.404	See	See
4	LogisticRegression	predict	1000	100	10000	[26]	0.002	0.0	[136.14673581]	0.0	NaN	NaN	NaN	NaN	0.240	0.003	0.0	0.549	0.117	See	See
5	LogisticRegression	predict	1000	1	10000	[26]	0.000	0.0	[23.41266627]	0.0	NaN	NaN	NaN	NaN	0.000	0.001	0.0	0.135	0.116	See	See

Ridge: scikit-learn (1.0.dev0) vs. scikit-learn-intelex (2021.20210705.191215)

All estimators share the following hyperparameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	max_iter	random_state	r2_score	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
0	Ridge	fit	1000	1000	10000	NaN	0.173	0.0	0.461	0.0	NaN	NaN	NaN	0.175	0.0	0.989	0.0	See	See
3	Ridge	fit	1000000	1000000	100	NaN	1.443	0.0	0.554	0.0	NaN	NaN	NaN	0.230	0.0	6.264	0.0	See	See

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	max_iter	random_state	r2_score	mean_sklearnex	stdev_sklearnex	speedup	stdev_speedup	sklearn_profiling	sklearnex_profiling
1	Ridge	predict	1000	1000	10000	NaN	0.011	0.000	7.082	0.0	NaN	NaN	0.106	0.018	0.0	0.622	0.017	See	See
2	Ridge	predict	1000	1	10000	NaN	0.000	0.000	1.074	0.0	NaN	NaN	NaN	0.000	0.0	0.727	0.740	See	See
4	Ridge	predict	1000000	1000	100	NaN	0.000	0.001	1.973	0.0	NaN	NaN	1.000	0.000	0.0	1.846	3.792	See	See
5	Ridge	predict	1000000	1	100	NaN	0.000	0.000	0.014	0.0	NaN	NaN	NaN	0.000	0.0	0.628	0.741	See	See